Computer system and method for causality analysis using hybrid first-principles and inferential model

ABSTRACT

Computer-based methods and system perform root-cause analysis on an industrial process. A processor executes a hybrid first-principles and inferential model to generate KPIs for the industrial process using uploaded process data as variables. The processor selects a subset of the KPIs to represent an event occurring in the industrial process, and divides the selected data into time series. The system and methods select time intervals from the time series based on data variability and perform a cross-correlation between the loaded process variables and the selected time intervals, resulting in a cross-correlation score for each loaded process variable. Precursor candidates from the loaded process variables are selected based on the cross-correlation scores, and a strength of correlation score is obtained for each precursor candidate. The methods and system select root-cause variables from the selected precursor candidates based on the strength of correlation scores, and analyze the root-cause of the event.

RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/141,701, filed on Apr. 28, 2016, which claims the benefit of U.S.Provisional Application No. 62/156,024, filed on May 1, 2015. Theteachings of the above applications are incorporated herein by referencein their entirety.

BACKGROUND OF THE INVENTION

Although normal chemical or petro-chemical plant operations arecontrolled using Advanced Process Control (APC) and are optimized withReal-Time Optimization (RTO), a large number of undesirable plant eventsstill occur in processes at chemical or petro-chemical plants, whichcost the process industry billions of dollars per year. Theseundesirable plant events include unexpected unit operation breakdown orplant shutdown due to equipment problems, feed materials quality change,faulty sensors/actuators, and human operation errors. Because of thelarge number of undesirable plant events, the development ofroot-cause-analysis technology that leads to quick and efficientidentification of the root causes of these events would be of extremebenefit to the process industry. However, chemical and petro-chemicalplants measure a formidable number of process variables in relation toplant events. As such, performing root-cause-analysis on a particularplant event using the historian dataset for these measured processvariables presents a challenge for process engineers and operators.Prior art systems lack tools for quickly and efficiently performingroot-cause-analysis on such a formidable number of measured processvariables.

Further, prior art systems lack effective online models, such as firstprinciples models, to calculate event indicators for identifyingparticular process variables to use in root-cause-analysis of a plantevent. First principles models have been widely used offline inpetroleum, chemical, and process industries for process design,simulation, and optimization over the last 30 years because of theiraccuracy and transparency in fundamental physical and chemicalprinciples. Commercial engineering software for offline applicationsusing first principles models have advanced tremendously over the last30 years, and during this time, efforts have been made to also use firstprinciples models online for real-time applications, such as onlineprocess optimization and control. First principles models have manywell-known advantages over black-box models that are typically usedonline. These advantages include being more rigorous and reliable forsimulating and predicting process behavior, providing broader coverageof complex nonlinearities, and providing better extrapolations. Usingfirst principles models online to calculate or predict key performanceindicators (KPIs) has been a long time goal for many process engineersand operators. However, efforts to use a first principles model onlinefor real-time event prediction, prevention and root-cause-analysisapplications have been a challenging task, and for prior art systemsthere still exists a gap between theory and practice.

SUMMARY OF THE INVENTION

The present invention addresses the difficulties in performingroot-cause-analysis for a typical plant process involving a formidablenumber of process variables. The difficulties for prior art systems toperform root-cause-analysis come from several factors. First,undesirable plant events in a process at a chemical or petro-chemicalplant are challenging for prior art systems to analyze because theevents may not occur consistently, or even be repeatable, in the processhistory, and are often discrete in time. As such, prior art systems arenot able to determine direct correlations between the events and thecontinuous plant operational historian data from the process. Second, atypical process unit consists of thousands of measurable variables, anddetermining specific key root-cause variables from such a large numberof process data is a daunting task for process engineers and operators.Third, even though only a small number of process variables may beroot-cause variables for the event, prior art systems lack efficienttools for performing the task of calculating the causal correlationstrength of each process variable to the event based on relativesensitivities, contributions, and event lead-time, as required toidentify the process variables as potential root-cause variables.

Embodiments of the present invention first address several difficultiesin using first principles models online for root-cause-analysis. First,a full scale of a first-principles dynamic model for plant off-linesimulation was usually constructed with very high complexity, typicallyconsisting of a formidable number of variables and model parameters(e.g., 12,000 variables plus 12,300 parameters for a single C2 splitterdistillation column). Applying such a full scale first-principle dynamicmodel to online applications will be difficult and costly in term oftime and efforts needed. Second, the model needs to be calibrated withplant operational data, but sufficient raw plant operational data neededto calibrate such a complex dynamic model may not be available, asusually only limited historical process data can be used for modelcalibration. To reduce the complexity of using a full-scale dynamicmodel and lower the requirements on plant data availability, asteady-state first-principles model is instead used in embodiments ofthe present invention.

Further, once the steady-state first-principles model was calibrated,the online prediction may only be generated at a steady-state. That is,the first-principles model only provides discrete-time (non-continuous)calculations when the process detects reaching a steady-state. As such,the calculation or estimation/prediction of KPIs from thefirst-principles models are valid only when the process/model reaches asteady-state, but steady-states can be found only at certain periodsover the plant historical operation data. Therefore, usually onlyscattered data points over the time series of multivariate processvariables are qualified and used to calculate model/predict steady-stateKPIs. In practice, the continuous calculation or estimation of variousprocess KPIs are more desirable, for example, a distillation column'sflooding risk factor will be extremely important for an operator towatch and monitor continuously, such that once the risk factor getsclose to a critical threshold, an early warning may be triggered, andcorresponding actions may be taken so that the unwanted plant shutdowndue to column flooding can be prevented timely.

The present invention is directed to a computer system and methods forperforming root-cause-analysis of undesirable plant events. As the firststep, embodiments of the present invention are directed to the use of ahybrid online first principle model combined with in empiricalinferential model to generate continuous KPIs for representing theundesirable plant events. For example, the undesirable event of adistillation column flooding may be represented by flooding risk factorsKPIs generated online by a hybrid first principles model. In someembodiments, other measurements may be used together with KPIs, orinstead of KPIs, to represent undesirable process events.

The computer system and methods may build, calibrate, and deploy ahybrid first principles and inferential model online to generatecontinuous KPIs for representing plant events. The present inventionprovides an approach that allows preserving the advantages of firstprinciples models for real-time applications. Unlike prior approaches,the system and methods of the present invention combine the traditionalfirst principles model for reliable and accurate KPI calculation atsteady-state, with an empirical inferential model for continuousestimation or prediction of the KPIs between the steady-state operationpoints. In this way, a hybrid model may provide reliable, accurate andcontinuous KPI value estimations in an online application. The inventionallows process engineers and operators to deploy numerous well-developedfirst principles models online for KPI calculation and real-timeprediction estimation, providing a powerful solution to many issuesfaced at the plant.

In this approach, the first-principles model and the inferential modelare first constructed in an offline mode for model calibration andinferential building. Specifically, a scalable first principles modelfor a single process unit (e. g., distillation column, reactor, furnace,and the like) is built and calibrated with plant historical operationdata and initiated from a plant database (i.e., historian database orhistorian). A dataset consisting of model required measurements from aplant operation historian is retrieved and auto-data-slicing is appliedto the dataset for data preprocessing and data selection (see U.S. Pat.No. 9,141,911 B2, which is incorporated herein by reference in itsentirety). Further, a model calibration procedure is implemented tocalibrate the first-principles process model based on the dataset, and asteady-state detection module is used to find out where the unit(process) reached a steady-state among the dataset. Once afirst-principles model is calibrated, the model is used to generatesteady-state KPI values, which are usually unmeasured or difficult tomeasure, but very important for the process engineer/operator to keepthe process operation safe or at an optimal operation condition, such asdistillation columns' flooding risk factor, column efficiency, outputproduct quality, reactors' conversion efficiency or a furnace energyconsumption rate, etc. In order to overcome the limitations of a singlefirst-principles model between steady-state periods, a complementaryinferential model acts as a bridge to generate estimations of KPIsbetween any two steady-state periods. This resolves the continuous KPIestimation problem and works with the first-principles model in asynergistic way. The inferential model is built with a partial leastsquares (PLS) linear or neural network (NN) nonlinear model by using thesteady-state KPI data as model output and selected measurable processvariables data as model inputs from a historical operation dataset.

Then, the models are used together online as a hybrid model or analyzerto generate continuous KPIs as output. The hybrid model or analyzer maygenerate continuous KPIs to represent the closeness measure to plantevents, including undesirable plant events, as part of a root-causeanalysis system. The system and methods further provide for periodiconline model calibrations and automatic inferential model adaptationsfor maintaining the model when the process operation scheme changes. Inthis manner the present invention may provide up-to-date process KPIs toreflect the changes in process equipment and/or operation conditions. Asa result, the unit (process) operation becomes more transparent toprocess engineers and/or operators than before and enable them toaddress many practical operational performance issues of today, such aspreventing column flooding and avoiding unwanted plant shutdown.

The computer system and methods may then use the continuous KPI valuesrelevant to a particular undesirable plant event (i.e., event-relevantKPIs) to search for precursor candidates for the undesirable plant eventfrom the thousands of process variables measurements. The search may beperformed by a search engine configured as part of a root-causeanalyzer, which is communicatively coupled to the hybrid model. A KPImay be determined as an event-relevant KPI because it highly correlatesto the particular undesirable plant event, such as selecting theestimated flooding factor of a distillation column KPI for columnflooding events or selecting the column separation efficiency KPI forcolumn fouling events. The event-relevant KPIs may be used to indicatethe risk level of the undesirable plant events, and further tofacilitate the search (e.g., a target signal precursor search (TSPS)) toidentify precursor variable candidates among the thousands of processvariable measurements potentially related to the undesirable events inthe plant historian. To perform a search, the system and methods mayfirst divide values of the event-relevant KPIs into multiple sets oftime series, and time intervals of each subset of time series may beselected for searching for precursor candidates for the event (i.e.,precursor candidates) based on data variability in the time interval.The time intervals may be defined based on large value variations in thedata over time or different operating levels for the subset of KPIs.Note, the system and methods may load the process variables measurementsfrom the historian database (i.e., the historical operation dataset) asthe precursor candidates for a specific undesirable plant event. Aspecial data screening and pre-processing method (see e.g., U.S. Pat.No. 9,141,911 B2, which is incorporated herein by reference in itsentirety) may be applied to the historical operation dataset to obtainauto-preprocessed clean data for the precursor search.

The system and methods may then perform a cross-correlation analysisbetween the loaded process variables and the event-relevant KPIs of theselected time intervals, and thereby calculate a correlation score foreach loaded process variable. The system and methods may perform thecross-correlation analysis by first performing an elimination of one ormore loaded process variables based on a global correlation threshold.The system and methods may then calculate correlation scores for theprocess variables remaining after the first elimination, wherein theinitial cross-correlation scores are calculated by evaluating thepositive and negative intervals for each respective time interval. Thesystem and methods may then accumulate the cross-correlation scores foreach remaining process variable such that the accumulatedcross-correlation score for each remaining process variable iscalculated over all the selected time intervals, wherein performing asecond elimination of one or more of the remaining process variablesbased on the accumulated cross-correlation scores. The system andmethods may select precursor candidates from the process variablesremaining after the second elimination. The selection of the precursorcandidates may be based on calculating rolling lag times over the entiretime series for each of the remaining process variables usingmulti-window correlations, which allows quick eliminations of manyprecursor candidates from the thousands of loaded process variables.

As a result of the precursor search, only a small set of the loadedprocess variables are found highly-correlated to the KPIs, which arethen identified as precursor candidates for continued analysis. Thesystem and methods may then apply qualitative and quantitative analysisto more granularly investigate the identified precursor candidates todetermine the impacts of each candidate. The analysis includes buildinga quantitative parametric dynamic model, such as a multiple-input singleoutput (MISO) dynamic parametric model, between the candidate precursorsand event-relevant KPIs to identify root-cause variables from theprecursor candidates. The analysis may be performed by a finalparametric dynamic analyzer configured as part of a root-cause analyzer,which may be communicatively coupled to the search engine and hybridanalyzer also configured as part of the root-cause analyzer. Prior tobuilding the model, the system and methods may analyze x-y linearrelationships between the selected precursor candidates and the subsetof KPIs, wherein a transform may be applied to any of the selectedprecursor candidates determined as having high nonlinearity to the KPIsbased on the analysis. The system and methods may then build thequantitative parametric dynamic model with the selected precursorcandidates as input and the subset of KPIs as output. The parametricmodel may be structured as a linear state space model, partial leastsquares (PLS) linear model, or piecewise linear model. Using theparametric model, the system and methods estimate dead-times and dynamiclinear filters for each input channel of the parametric model. Thedead-time may be estimated using an optimization search based on inputto the subset of KPIs. Further, the dynamic linear filters may beestimated using a linear reduction technique to determine optimallow-order model fittings.

Then using the estimated dead-times and filters, the system and methodsmay rebuild the final parametric model as a partial least squares (PLS)model to perform quantitative analysis of the selected precursorcandidates. More specifically, using the PLS model, the system andmethods may calculate a score for each selected precursor candidatebased on strength of correlation to the subset of the KPIs, includingrelative sensitivities, contributions, and event lead-time for theselected precursor variable candidates. The strength of correlationscore may be calculated using PLS regression and sensitivity analysistechniques. The system and methods may select root-cause variables fromthe selected precursor candidates based on the respective calculatedstrength of correlation score. In addition, the selected precursorcandidates may be sorted and ranked according their strength ofcorrelation score, and may be presented, along with their relativesensitivities, contributions, and lead-times to process engineers andoperators for confirmation and rejection as root-cause variables.

Using the ranked list as a signature in diagnosing plant operationdefects, the process engineers and operators may quickly further narrowthe results of the root-cause search and determine whether each systempresented root-cause variable is the actual cause of an operationproblem based on their knowledge of the process. Further, the processengineers and operators may also use this information to betterunderstand unexpected events, focus the investigation of an undesirableplant event on the system presented root-cause variables, and take earlyactions, if necessary, based on the KPI-related monitoring and alarming.As a result, the information may lower the risks of reoccurrence of theevents, and ultimately prevent the undesirable events from futureoperations and reduce the economic loss in manufactory plants. As such,the system and methods provide process engineers and operators with ahighly efficient tool for event causality analysis in various processindustry applications, including but not limited to, undesirable eventsroot-cause analysis, operational trouble-shooting, and process faultsdetection and identification, as well as plant risk management.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particulardescription of example embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingembodiments of the present invention.

FIG. 1 illustrates a block diagram depicting an example networkenvironment for data collection and monitoring of a plant process inembodiments of the present invention.

FIG. 2A illustrates a flowchart of an example method for a hybridanalyzer to generate KPIs in embodiments of the present invention.

FIG. 2B illustrates a block diagram depicting an example configurationof the hybrid analyzer in FIG. 2A.

FIG. 3A illustrates a block diagram of another example configuration ofa hybrid analyzer calculating KPIs in embodiments of the presentinvention.

FIG. 3B illustrates a block diagram depicting example concept andfunctions of the hybrid approach of a first principles model and aninferential model in an example embodiment of the hybrid analyzer inFIG. 3A.

FIG. 3C illustrates a data chart of plots from a first principles modeland an inferential model together calculating KPIs according to thepresent invention.

FIG. 4 illustrates a flowchart of an example method for performing atarget signal precursor search (TSPS) in embodiments of the presentinvention.

FIG. 5A illustrates a flowchart of an example method for performing acausality analysis (CA) in embodiments of the present invention.

FIG. 5B illustrates a block diagram depicting an example multiple inputsingle output (MISO) model used in some embodiments of FIG. 5A.

FIGS. 6A-6I illustrate an application example of a C2 splitter depictingthe performance of root cause analysis using the example methods inFIGS. 2A, 4, and 5A-5B.

FIG. 7 is a block diagram of a computer (or digital processing) systemfor performing root-cause-analysis according to at least one embodimentof the present invention.

FIG. 8 is a schematic diagram of an example computer network environmentin which embodiments of the invention may be implemented.

DETAILED DESCRIPTION OF THE INVENTION

A description of example embodiments of the invention follows. As usedherein “Partial Least Squares” and “Projection Latent Structure” areused interchangeably and are both referenced by the abbreviation “PLS”.

In example embodiments, the present invention may use continuous KPIs asindicators in performing root-cause-analysis of undesirable plantevents. In some of these example embodiments, the present invention mayuse a hybrid analyzer including a primary first principles model oranalyzer (i.e., first principles model) and a secondary dynamicinferential model or analyzer (i.e., inferential model) connected inparallel to calculate the continuous KPIs. In other example embodiments,(1) the hybrid analyzer may include a first principles model coupledwith a different secondary model, (2) the hybrid analyzer may beconfigured with the models connected in other non-parallelconfigurations, and (3) the models may calculate other measures thanKPIs. In yet other embodiments, the present invention may use otheranalyzers, which includes other models besides first principles models,to calculate the KPIs or other estimations of certain properties of anindustrial process as indicators.

Example Network Environment for Plant Processes

FIG. 1 illustrates a block diagram depicting an example networkenvironment 100 for monitoring plant processes in embodiments of thepresent invention. System computers 101, 102 may operate as a root-causeanalyzer. In some embodiments, each one of the system computers 101, 102may operate in real-time as the root-cause analyzer of the presentinvention alone, or the computers 101, 102 may operate together asdistributed processors contributing to real-time operations as a singleroot-cause analyzer. In other embodiments, additional system computers112 may also operate as distributed processors contributing to thereal-time operation as a root-cause analyzer. In some embodiments,system computers 101, 102, 112 operating as the root-cause analyzer maybe configured with a hybrid model, search engine, and a parametricanalyzer. The hybrid model may be configured with a first principlesmodel and an inferential model. The parametric analyzer may beconfigured with a multiple-input single output (MISO) dynamic parametricmodel.

The system computers 101 and 102 may communicate with the data server103 to access collected data for measurable process variables from ahistorian database 111. The data server 103 may be furthercommunicatively coupled to a distributed control system (DCS) 104, orany other plant control system, which may be configured with instruments109A-109I, 106, 107 that collect data at a regular sampling period(e.g., one sample per minute) for the measurable process variables,106,107 are online analyzers (e.g., Gas Chromatographs or GC) thatcollect data at a longer sampling period. The instruments maycommunicate the collected data to an instrumentation computer 105, alsoconfigured in the DCS 104, and the instrumentation computer 105 may inturn communicate the collected data to the data server 103 overcommunications network 108. The data server 103 may then archive thecollected data in the historian database 111 for model calibration andinferential model training purposes. The data collected varies accordingto the type of target process.

The collected data may include measurements for various measureableprocess variables. These measurements may include a feed stream flowrate as measured by a flow meter 109B, a feed stream temperature asmeasured by a temperature sensor 109C, component feed concentrations asdetermined by an analyzer 109A, and reflux stream temperature in a pipeas measured by a temperature sensor 109D. The collected data may alsoinclude measurements for process output stream variables, such as theconcentration of produced materials, as measured by analyzers 106 and107. The collected data may further include measurements for manipulatedinput variables, such as reflux flow rate as set by valve 109F anddetermined by flow meter 109H, a re-boiler steam flow rate as set byvalve 109E and measured by flow meter 109I, and pressure in a column ascontrolled by a valve 109G. The collected data reflect the operationconditions of the representative plant during a particular samplingperiod. The collected data is archived in the historian database 111 formodel calibration and inferential model training purposes. The datacollected varies according to the type of target process.

In FIG. 1, the system computer 101 and 102 may execute a firstprinciples model and inferential model for online deployment purposes.The output values generated by the hybrid model on the system computer101 may provide to the instrumentation computer 105 over the network 108for operator to view, or may be provided to automatically program anyother component of the DCS 104, or any other plant control system orprocessing system coupled to the DCS system 104. Alternatively, theinstrumentation computer 105 can store the historical data 111 throughthe data server 103 in the historian database 111 and execute the hybridmodel in a stand-alone mode. Collectively, the instrumentation computer105, the data server 103, and various sensors and output drivers (e.g.,109A-109I, 106, 107) form the DCS 104 and work together to implement andrun the presented application.

The example architecture 100 of the computer system supports the processoperation of the present invention in a representative plant. In thisembodiment, the representative plant may be a refinery or a chemicalprocessing plant having a number of measurable process variables such astemperature, pressure and flow rate variables. It should be understoodthat in other embodiments the present invention may be used in a widevariety of other types of technological processes or equipment in theuseful arts.

Hybrid Analyzer Process

FIG. 2A illustrates a flowchart of an example method 200 for a hybridanalyzer 260 to calculate KPIs. In some embodiments, the hybrid analyzer260 may be configured as part of a root-cause analyzer, such as onsystem computers 101, 102 of FIG. 1. The calculated KPIs may be relevantto particular plant events, such as undesirable plant events. The method200 begins by the hybrid analyzer 260 calibrating a first principlesmodel at step 210 of FIG. 2A. In some embodiments, the hybrid analyzer260 may select the first principles model from multiple available modelsfor calculating KPIs, and in other embodiments the hybrid analyzer 260may be provided with a single first principles model for calculatingKPIs. In the embodiment of FIG. 2A, the first principles model is asteady-state process/unit simulation model, but in other embodiments,the first principles model may be another simulation model. The firstprinciples model is calibrated using historical plant operation data astraining data to calculate process operation KPI values at asteady-state. In other embodiments, other plant operation data may beused as training data. The training data for the model calibration aregenerated by various components of a manufacturing plant and are sampledusing a plurality of sensors (e.g., 109A-109I, 106, 107) strategicallyplaced in the plant and are stored in a historian database 111 as plantoperation historian data, as shown in FIG. 1. In some embodiments, thetraining data may be stored in other locations in memory. The hybridanalyzer 260 may include a variable selection component for selectingone or more measurable process variables from the training data as inputdata for performing the model calibration.

Typically a first principles model may contain tens to thousands ofprocess variables, such as mass flows, vassal holdups, pressures andtemperatures of liquids and vapors at different locations of a process,and the like, and may contain from a few to millions of mathematicalequations and parameters, depending on the size and complexity of theunderlying process. In a general form, a first-principles steady-statemodel can be described in Equations (1) and (2), as follows:F(U,Y,X,θ)=0  (1)Subject to:G(U,Y,X,θ)≤0;  (2)where U=[u₁ u₂ . . . u_(r)]^(T), Y=[y₁ y₂ . . . y_(p)]^(T), X=[x₁ x₂ . .. x_(n)]^(T), θ=[θ₁ θ₂ . . . θ_(m)]^(T)

In Equations (1) and (2), U comprises the first principles model's inputvariables, Y comprises output variables, X comprises state variables,and θ comprises model parameters, respectively. The right side ofEquation (1) equals zero (i.e., 0), which means that thefirst-principles model of Equation (1) describes the relations, such asmass balances and energy balances, only at a steady-state (i.e., whenall inputs and outputs reached a steady-state balance and notime-dependent dynamic transitions were involved).

Calibration of the first principles model at Equation (1) is performedin the following sub-steps (by the hybrid analyzer 260 as part of method200, step 210) represented by Equations (3)-(6). First, the hybridanalyzer 260 receives a set of process measurements data of inputvariables Ū and output variables Y at multiple operation points when theprocess has reached respective steady-states. Second, the hybridanalyzer 260 manually, or using computer routines, tunes modelparameters θ in order to minimize the model prediction error asdescribed in reference to Equations (3)-(4) as follows:

$\begin{matrix}{{\min\limits_{\theta \Subset \Theta}{E\left( {\overset{\_}{U},\overset{\_}{Y},X,\theta} \right)}} = {\min{{\hat{Y} - \overset{\_}{Y}}}_{2}}} & (3) \\{{{Subject}\mspace{14mu}{to}\text{:}}{{{G\left( {\overset{\_}{U},\hat{Y},X,\theta} \right)} \leq 0};}} & (4)\end{matrix}$

Note, Equation 4 comprises various process constraints, such as themaximum capacity of an equipment when a flow control valve opened to100%, high and/or low limits of a process variables' operation, and thelike.

After calibration of the first-principles model, all values of modelparameters in θ are determined and fixed in Equation (1). The hybridanalyzer 260, then, calculates the specified KPIs based on Equations (1)and (2) from process input variables' values at each steady-state asfollows:KPI _(S) =H(U _(S) ,Y _(S) ,X _(S),θ)  (5)Subject to:G(U _(S) ,Y _(S) ,X _(S),θ)≤0;  (6)

Note, Equation (5) is a pre-defined process target KPI function, such asa flooding risk factor, column separation efficiency, and the like,depending on the subject process and the root-cause-analysis problem,and in Equation (5) the subscript “s” represents process at asteady-state.

The method continues by the hybrid analyzer 260 training an inferentialmodel at step 220 of FIG. 2A. The inferential model may be a linearmodel, such as a partial least squares (PLS) model, a piecewise-linearnonlinear PLS model or a preferred nonlinear neural-network (NN) typemodel (see U.S. Pat. Nos. 7,330,804 and 7,630,868 by Assignee, which areincorporated herein by reference in their entirety). In someembodiments, the hybrid analyzer 260 may select the inferential modelfrom multiple available models for calculating KPIs, and in otherembodiments the hybrid analyzer 260 may be provided with a singleinferential model for calculating KPIs. The inferential model is trainedusing the selected measurable process variables as input data, and theKPI's values calculated by the calibrated first principles model (of210) as output data. Then, the hybrid analyzer in step 230 uses thetrained inferential model (of 220) to generate continuous estimations oftargeted KPIs by continuously sampling at a consistent sampling rate(e.g., 1 sample per minute) with process variables as inputs.

In some embodiments, training the inferential model comprises thefollowing sub-steps (by the hybrid analyzer 260 as part of method 200,step 220). First, the hybrid analyzer 260 defines a specificinput-output inferential model as shown in Equation (7):y=f(u ₁ ,u ₂

,u _(r) ,θ,t)  (7)where u₁, u₂, . . . u_(r) are input variables, θ is a parameter vector,and y is output variable.

Second, the hybrid analyzer 260 collects input and output variables datafrom process measurements and calculated KPIs. The input variables datamay be continuously sampled from process measurements in a format oftime-series, while the output variable data may be continuous ornon-continuous measurements such as online analyzer measurements, labsamples, or calculated KPIs at different steady-states. Third, thehybrid analyzer 260 selects a model structure, such as a linear PLS,piece-wise-linear model, neural nets (NN) nonlinear model, or a BDN(bounded-derivative-net) model, and the like, depending on theunderlying defined problem. Fourth, the hybrid analyzer 260 divides theavailable dataset into two groups: a training set and a validation setin a percentage (e.g., 70% for training and 30% for validation) in afixed or random selection. Fifth, the hybrid analyzer 260 fits the modelwith training set of input and output data, and, then, use thevalidation dataset to validate the model, so as to minimize the model'sprediction error, as shown in Equation 8 as follows:

$\begin{matrix}{{\min\limits_{\theta \Subset \Theta}{E\left( {\hat{y},u_{1},{\ldots\mspace{14mu} u_{r}},\theta,t} \right)}} = {\min{\sum{{{y(t)} - {\hat{y}(t)}}}^{2}}}} & (8)\end{matrix}$

As a result, the trained inferential model may be used for KPI outputcalculation in the Equations (9) and (10) as follows:ŷ=f(u ₁ ,u ₂

,u _(r) ,θ,t)+bias(t)  (9)bias(t)=(1−α)×bias(t−1)+α×(y(t−1)−ŷ(t−1)))  (10)

Note Equation (10) is a recursive prediction bias update scheme(described in details later in reference to FIG. 3A), and the bias(t) isan offset to compensate process shifts. Using Equations (9) and (10),the hybrid analyzer 260 calculates continuous KPIs as an output variableof the inferential model offline or online.

The calibrated and trained hybrid analyzer resulting from 210 and 220 isthen deployed online to provide effective monitoring and fault detectionon those targeted KPIs. Further, in an online running mode, the hybridanalyzer 260 provides a readily self-adaptable framework to re-calibratethe first principles model and update the inferential model for apersistent performance.

Once the hybrid analyzer 260 is running online, in step 240 of FIG. 2A,the first principles model calculates KPIs when the process reaches asteady state. That is, a steady-state detection module of the hybridanalyzer 260 monitors all model inputs, and triggers a calculation ofnew KPI values whenever the process reaches a new steady-state. The KPIcalculations over each pre-specified time period continues until theprocess steady-state condition is changed and the process is detected tobe in a non-steady-state operation phase again. In parallel, once newvalues of KPI are calculated at steady-state using Equations (5) and(6), and made available to the inferential model from Equation (7), theinferential model dynamically takes those new KPI values as new outputmeasurements. Then, in step 250 of FIG. 2A, the inferential modelperforms prediction bias updates using Equations (9) and (10) to correctthe current dynamic KPI predictions by modifying KPI's offset. Thisallows the process engineers and operators to readily monitor usuallyunmeasurable KPIs and KPI related events in the plant based on theoutput of the hybrid analyzer 260. Further, the hybrid analyzer 260includes steady-state auto-detection and inferential bias adaptationcapabilities, which allows the hybrid analyzer to address thereliability of the process model output over a wide operating range.Together, the first principles model and the inferential model mitigatethe disadvantages, and enhance the advantages of each modelingmethodology when used alone.

Hybrid Analyzer Configuration

FIG. 2B illustrates a block diagram depicting an example configurationof a hybrid analyzer 260, such as the hybrid analyzer described in FIG.2A. The system computers 101, 102 of FIG. 1 may be configured as aroot-cause analyzer including the hybrid analyzer 260. That is, eachsystem computer 101, 102 may be configured according to this exampleconfiguration or the example configuration may be distributed across thesystem computers 101, 102, and 112. Note that the example configurationdepicted in FIG. 2B may be implemented as software in some embodiments,as hardware in other embodiments, and as a combination of software andhardware in yet other embodiments.

The first principles development model 268 and the inferentialdevelopment model 267 may operate in parallel, and may be calibrated andtrained using the procedures as follows. As shown in FIG. 1, data formeasureable process variables may be collected from various samplingpoints in an operational plant (e.g., plant sensors 109A-109I, 106, 107of DCS 104) and stored in the historian database 111. The data server103 retrieves the collected data for the measurable process variablesfrom the historian database 111, and communicate the collected data tosystem computer 101 configured as the hybrid analyzer 260 of FIG. 2B.The hybrid analyzer 260 receives the process data 261 from the dataserver 103, and sends the process data 261 to the variable selection andpreprocessing module 262. The variable selection and preprocessingmodule 262 performs a variable selection process, where some or all ofthe measurable process variables from plant sensors 109A-109I, 106, 107are selected. The variable selection and preprocessing module 262 maysample the measurable process variables on a continuous basis, or maysample the measurable process variables at pre-determined time periodsbased on matching a timestamp corresponding to a steady-state. Themeasurable process variables may also be suitably screened by a dataselection apparatus (see U.S. Pat. No. 9,141,911 B2 (by Assignee), whichis hereby incorporated by reference in its entirety). After performingvariable selection, the variable selection module 262 stores themeasurable process variables' settings in the steady-state detectionmodule 263.

The steady-state detection module 263 sends the stored measurableprocess variables settings 264, 269 to the first principles developmentmodel 268 for calibration. Note, in the embodiment of FIG. 2B, the firstprinciples model is configured as two separate modules, the firstprinciples development model 268 for performing offline calibrationfunctions and the first principles run-time model 271 for performingonline functions. In other embodiments, the first principles model maybe configured as one module for performing all related functions. Duringthe calibration process, the derived data parameters from the firstprinciples development model 268 are output to the model parametermodule 270 for storage. The model parameters module 270 then uses theknowledge gained from the calibrating process to derive run-time modelvariables from the stored output. The model parameters module 270provides the run-time model variables to the first principles run-timemodel 271 for online run-time deployment.

The output 272 of the first principles run-time model 271, together withthe output 265 of the variable selection and preprocessing module 262,is provided as training data to the inferential development model 267.Note, in the embodiment of FIG. 2B, the inferential model is configuredas two separate modules, the inferential development model 267 forperforming offline model training and the inferential run-time model 273for performing online functions. In other embodiments, the inferentialmodel may be configured as one module for performing all relatedfunctions. Also note that the same set of process data 261 is used tocalibrate the first principles development model 268 and train theinferential development model 267. The inferential development model 267uses the training data to derive run-time parameters that may beprovided to the run-time model 273 for online run-time deployment.

The output of the trained inferential run-time model 273 is provided toother system computers in the network environment 100 of FIG. 1 ascontinuous KPIs for performing process operation KPI monitoring androot-cause analysis in the plant. Typically, the data server 103provides distributed processing units, for interacting with the DCScomponents 104 and the historian database 111, which provides for datacollection and archival storage of plant historical data. Typical dataarchived by the data repository includes information regarding variousprocess variables, including status, values, operators' event messages,sequences of event data, laboratory data, pre-event and post-event data.Certain archived data may be pre-screened by the variable selection andpreprocessing module 262 before being presented to the inferentialrun-time model 273.

During the ongoing operation of the process, the data stored in thesteady-state detection module 263 may be loaded into the run-timevariables module 266, which in turn may be loaded into the deployedfirst principles run-time model 271. Similarly, the data from the modelparameters module 270 may be loaded into the deployed first principlesrun-time model 271. Once the configuration data and parameters have beenloaded into the modules 266, 271, the variable selection andpreprocessing module 262 receives additional process data 261 from dataserver 103. The variable selection and preprocessing module 262 performsvariable selection on the process data 261 and stores the output data inthe steady-state detection module 263. The output data is also loaded asinput data variables 265 at the deployed inferential run-time model 273.Note, the internal settings of the first-principles run-time model 271and the inferential run-time model 273 are stored at the modelparameters module 270. The deployed inferential run-time model 273 mayalso use the loaded input data variables 265 to generate a continuousestimate of KPIs, which are output to the network environment 100.Further, the deployed first-principles run-time model 271 performssteady-state calculations using the loaded data, when the processreaches a steady-state, and provides output of the calculations to thedeployed inferential run-time model 273. The deployed inferentialrun-time model 273 uses the received output calculations to perform theprediction bias updates to correct the calculations of the continuousKPI estimations provided to the network environment 100.

KPI Calculations

FIG. 3A illustrates a block diagram depicting another configuration 300of a hybrid analyzer calculating KPIs. In some embodiments, FIG. 3A mayillustrate a simplified representation of the hybrid analyzer 260 inFIG. 2B. The hybrid analyzer 300 includes a first principles(steady-state) model 302 and an inferential model 304 connected inparallel. Note, the first principles model 302 comprises both the firstprinciples development model 268 and first principles run-time model 271of FIG. 2B. Further note, the inferential model 304 comprises both theinferential development model 267 and inferential run-time model 273 ofFIG. 2B.

In this example embodiment, the hybrid analyzer 300 is configured tocalculate KPIs relevant to undesirable plant events (e.g., distillationcolumn flooding). The undesirable plant event may have occurred in thepast or are yet to occur in the future, and the calculated KPIs may beused as indicators of the plant events. In the embodiment of FIG. 3A,the hybrid analyzer 300 has previously calibrated the first principlesmodel 302 and trained the inferential model 304, and both models 302,304 are in run-time mode. The hybrid analyzer 300 receives process data261 which is provided to the first principles model 302 and theinferential model 304. In the embodiment of FIG. 3A, the processing ofthe first principles model 302 output is controlled by the hybridanalyzer using a mode switch module 308. When the switch 308 is atposition A (as shown in FIG. 3A), the steady-state KPIs (calculated fromEquations (5) and (6)) from the first principles model 302 are providedto the inferential model 304 for use in training inferential model. Whenthe switch 308 is at position B, the inferential model is in run-timemode and generates continuous KPC predictions. Note, the dynamic KPIpredictions (calculated by Equations (9) and (10)) are output to aprocess monitoring environment, such as the environment 100 in FIG. 1,the dynamic KPI predictions are provided as input 306 to adder 316 to beadjusted by a bias using adaptive filter 314.

When the mode switch 308 is in position B, the steady-state KPIs fromthe first principles model 302 are instead provided to the adder 310. Asthe inferential model 304 calculates the dynamic KPI predictions, andthe KPI predictions are provided as input 306 to adder 316 for biasadjustment, the dynamic KPI predictions after adder 316, are furtherprovided as input 312 to adder 310 as a feedback. At adder 310, thedynamic KPIs biases are calculated with the steady-state KPIs that werealso provided to adder 310 (e.g., e_(k)=[Dyn_KPI(t)−SS_KPI(k)]) togenerate an updated bias for adjusting dynamic KPI predictions. Theupdated bias is then provided to an adaptive filter 314 for adjustingfuture KPI predictions to count for measurement offsets. The adaptivefilter 314 may be any conventional adaptive filters used in the processindustry. The final system output of hybrid analyzer configuration 300is dynamic KPI predictions that are corrected and filtered to accuratelyreflect an estimation of the process dynamic KPIs.

FIG. 3B illustrates a first principles model 302 in an exampleembodiment of the hybrid analyzer 300 of FIG. 3A. In FIG. 3B, historicaldata for different process variables (feedstock variability, energyconstraints, upstream unit performance, utility variability, effects ofPID, APC, and RTO controls, product requirement variability, anddownstream unit performance) are loaded into the hybrid model 302, 304from an historian database. These process variables may be used tocalibrate the first principles model 302 shown in FIG. 3B. Once thefirst principles model is calibrated, the process variables may then beused by the first principles model 302 to predict unmeasured KPIs for aparticular event, validate all the measured KPIs for the particularevent, and provide physical relationships with the KPIs duringsteady-states. For example, as shown, the first principles model 302 maybe passed feed conditions and compositions input from the processvariable measurements into the model, and based on pressures (sensed ordetected), temperatures (sensed or detected), calculated bottom rates,calculated distillate rates, and reflux conditions (sensed or detected)of the plant, the first principles model 302 may predict unmeasured KPIvalues for the particular event during steady-states. FIG. 3B also showsthe data analytics (trends, patterns, and inferential models) 321 usedby the hybrid model 300 to provide historical context for measured andpredicted KPIs for the particular event, estimate KPIs betweensteady-states, and discover relationships not included in the firstprinciples modeling scope. Together, the first principles modeling andthe data analytics allow for the example hybrid model 300 tocontinuously generate KPIs for the particular event.

FIG. 3C illustrates a data chart of exemplary plots 320 from a firstprinciples model 302 and an inferential model 304 that togethergenerated KPIs. The calibrated first principle model 302 (i.e.,steady-state model) online calculates steady-state KPI values over thehistorical data. Data chart 320 shows small circles to represent thecalculated KPIs from the calibrated first principle model 302. As shownin FIG. 3C, the first principles model 302 only calculates valid KPIvalues (i.e., small circles), and provides the valid KPIs to theinferential model 304 for training, when the process is detected to bein a steady-state. In the steady-state period, the inferential model 304is trained using the valid KPIs represented by the small circles andcorresponding measured process variable inputs at these periods. Oncethe inferential model 304 is trained, the trained inferential model 304,now in run-time mode, is fed continuous operations data as input, andgenerates continuous KPI estimations (i.e., the continuous curves asshown in FIG. 3C). The calibrated first principle model 302 onlycalculates current KPIs (i.e., small circles) during periods that theprocess is in steady-state. As the time moves into the future, thetrained inferential model 304 continues to estimate/predict the KPIs,and the calibrated first principle model 302 continues to calculatecurrent KPIs in steady-state, for as long as operations data is fed asinput to the calibrated first principle model 302.

Target Signal Precursor Search

FIG. 4 illustrates a flowchart of an example method 400 for performing apreliminary target signal precursor search (TSPS) to identify precursorvariables of an undesirable plant event. The method 400 may be performedby a search engine that may be configured on system computer 101, 102,along with a hybrid analyzer 260,300, as part of a root-cause analyzer.Prior to performing this method, the hybrid analyzer may have generatedthe continuous KPIs in relation to one or more plant events. Note, inthis embodiment, the continuous KPIs have been generated using thehybrid analyzer 260, 300 as described in FIGS. 2A, 2B, and 3A-3C, but inother embodiments the continuous KPIs, or other such measurements, maybe generated using other models or analyzers. The main challenge inidentifying precursors for an undesirable plant event is the thousandsof process variables available (i.e., precursor candidates) in thehistorian database for the plant process. In the method of FIG. 4, thesearch engine of the root-cause analyzer may use the event-relevant KPIsfor facilitating the search of the process variables (by a searchengine) to select precursor candidates for the event.

The method 400 begins at step 410 by the search engine loading processvariables from a historian database. The search engine loads the processvariables as candidate precursors of a particular undesirable plantevent. Note that a special data screening and pre-processing method (seeU.S. Pat. No. 9,141,911 B2 by Applicant, which is incorporated herein byreference in its entirety) may also be applied to process variable toobtain auto-preprocessed clean data for the precursor search. At step410, the search engine also loads KPIs calculated by the hybrid analyzerto represent plant events. The search engine may select a subset of theKPIs to indicate the risk level that the particular undesirable plantevent has or will occur in the plant process. A KPI may be selected aspart of the subset (i.e., an event-relevant KPI) because the KPI highlycorrelates to the particular undesirable plant event, such as selectingthe estimated flooding factor of a distillation column KPI for columnflooding events or selecting the column separation efficiency KPI forcolumn fouling events. The search engine may load data for the processvariables and KPIs into various formats for analysis. In the embodimentof FIG. 4, the data is plotted in a data chart as time series curves,but in other embodiments, the data may be analyzed in any othergraphical or non-graphical formats known in the art.

At step 420, the search engine splits each respective KPI into a timeseries based on analyzing the behavior of the KPI. That is, the searchengine divides the data collected for the KPI over a time period intomultiple time intervals or segments (i.e., multiple subsets of timeseries). The time intervals may be defined by the search engine based onanalyzing the collected data to identify large value variations over thetime period. Several formulas may be applied for identification of largevalue variations. First, based on the multiple a of standard deviationσ_(Δx) of the difference between max and min values over M consecutivedata points, Equation (11) may be applied as follows:Δx _(i) >aσ _(Δx);Δx _(i)=max( x _(i,M))−min( x _(i,M));x _(i,M)=[X _(i) ,X _(i+1) ,X _(i+2) , . . . ,X _(i+M)]  (11)

Further, based on the multiple a of standard deviation σ_(Δx) of theabsolute slope value over M consecutive data points and K points foraveraging, Equation (12) may be applied as follows:δx _(i) >aσ _(δx);δx _(i)=mean( x _(i,K))−mean( x _(i+M−K,K));x _(i,K)=[X _(i) ,X _(i+1) , . . . ,X _(i+K)]x _(i+M−K,K)=[X _(i+M−K) ,X _(i+M−K+1) , . . . ,X _(i+M])  (12)

The time intervals may also be defined by the search engine based onanalyzing the collected data to identify KPI values at differentoperating levels of the process. In some embodiments, such as theembodiment in FIG. 4, the search engine may select time intervals havingthe highest variability from the time series for identifying candidateprecursors for the undesirable plant event. In other embodiments, thesearch engine may select time intervals based on additional or differentcriteria. During step 420, intervals in the time series withoscillations around steady-state are ignored, but the search engine mayuse these intervals for the calculation of cumulative scores forpositive and negative lags later in the method 400.

Using the selected time intervals, at step 430, the search engineperforms an initial elimination of process variables as part of thesearch process of potential precursors of the undesirable processbehavior. The initial elimination removes process variables with pooroverall correlations to the KPI, by the search engine only selectingprocess variable with global correlation above a specific threshold atthe selected time intervals. For example, the initial elimination can beperformed with a coarse requirement for global correlation to be largerthan 0.7. This elimination may involve the search engine analyzing thesubset time series to estimate the cross-correlations between theprocess variables and the KPI for the selected time intervals, anddiscarding variables with almost no correlations to the KPI.Cross-correlation is defined in a standard way as: ρ_(X,i,M,τ)=E[(x_(i,M)−mean(x _(i,M)))(x _(i,M)−mean(x _(i+τ,M)))]/σ _(x) _(i,M) σ _(x)_(i+τ,M) , where expression for x _(i,K) is defined in reference toEquations (11) and (12). The selected process variables (i.e., variablesremaining after the elimination) are candidate precursors for theundesirable plant events represented by the event-relevant KPI.

At step 440, the search engine then computes cross-correlation curvesfor the selected process variables over the selected time intervals.Specifically, consider a set of selected indices ī=[_(ξ),I_(ξ+1), . . .] that define areas of large value variations. A standardcross-correlation function versus time lag r may be calculated for thecorrelation analysis of each selected variable and KPI at each selectedtime interval defined by starting index I_(ξ) and length M. Thecalculation may include a constant time shift which is scaled based onthe temporal resolution of the time series. Then, the search enginecalculates a cross-correlation score for each cross correlation curve byevaluating the positive and negative integral of the respective crosscorrelation curve. At step 450, the search engine computes theaccumulated cross-correlation scores

$s = {\sum\limits_{\xi,\tau}\rho_{X,I_{\xi},M,\tau}}$for each selected variable over the selected time intervals. The searchengine computes the accumulated scores for a selected variable bydetermining the positive lag time τ>0 and negative lag time τ<0intervals from each cross-correlation curve for the respective selectedprocess variable X over all selected time intervals ī=[I_(ξ)I_(ξ+1), . .. ]. Note, the shape of a cross-correlation curve may be highlyirregular for realistic KPI time series, thus, the search engine may usean integral measure to adjust the positive and negative time lags. Twointegrals are computed: over positive lags and negative lags. For eachsemi-infinite domains and approximation is made to limit the range ofintegration to the largest value of time lag. Then the integral is

$S = {{\sum\limits_{\xi,\tau}{\tau\rho}_{X,I_{\xi},M,\tau}} \approx {\sum\limits_{\xi}{\int_{0}^{\tau_{\max}}{\rho_{X,I_{\xi},M,\tau}d\;{\tau.}}}}}$

At step 460, the search engine selects curves for all process variableswith an accumulated score above a configured threshold, therebyeliminating all process variables as candidate precursors with scoresbelow the configured threshold. This selection results in a dramaticreduction of the number of available candidates for precursors.

For each remaining process variable, at step 470, the search enginecomputes rolling lag time over the entire historical span of the timeintervals for the respective process variable. The search engine appliesthe rolling lag time computation

${S\left( {t;L} \right)} = {\sum\limits_{\tau}{\tau\rho}_{X,t,L,\tau}}$to each selected process variable curve, which includes applying aseries of cross-correlation curve constructions using a constant windowwidth L, starting from the past t=I₀ and moving toward the future. Thesearch engine performs the computations using the entire range ofhistorical data for a KPI, and for each cross-correlation curve, thesearch engine extracts the lag time based on the maximum time indicatedby the curve. The search engine sorts the process variables according tominimal cumulative lag time (i.e., highest negative lags to theevent-relevant KPI) as computed across the entire historical span. Thesearch engine selects the curves for the top sorted process variables tocontinue as precursor variable candidates, and the curves for the otherremaining precursor variable candidates are eliminated. In someembodiments, a user may configure a subrange of the historical span,instead of the entire historical span, to be used for computingcumulative lag time. Further, if a first precursor variable candidateshows strong correlation to a second precursor variable candidate, andthe second precursor variable candidate has a higher cross-correlationscore, then the search engine may eliminate the first precursor variablecandidate to prevent multi-collinearity effects. Method 400 of FIG. 4may be completed with the one or more precursor variable candidatesselected from the process variables initially loaded from the historiandatabase.

Note that FIG. 4 shows a sequence of steps with the increasing cost ofcalculations. For example, step 430 requires less computational effortsper variable than step 450, and step 450, in turn, requires lesscomputational efforts per variable than step 470. Thus, an essentialpart of method 400 is early and consistent elimination of variables fromthe list of potential precursors. This reduces a large of amount ofcomputation costs, makes the target signal precursor search analysispractical for industrial applications.

Causality Analysis

FIG. 5A illustrates a flowchart of an example method 500 for performinga causality analysis. The method 500 may be performed by a parametricanalyzer that may be configured on system computer 101, 102, along witha search engine and hybrid analyzer 260, 300, as part of a root-causeanalyzer. The method 500 begins at step 510 by the parametric analyzerloading process variables data from a historian database as measurementsof potential precursor variable candidates of a particular undesirableplant event. At step 510, the parametric analyzer 500 also loads KPIsthat highly correlate to the particular undesirable plant event andcalculated by the hybrid analyzer 260 (hybrid model) using Equations(5), (6), (9), and (10). The parametric analyzer 500 may perform similarsteps for method 500 (step 510) as described for the target signalprecursor search method 400 of FIG. 4 for initially identifyingprecursor candidates for the undesirable plant event based on the loadedKPIs. In some embodiments, the parametric analyzer 500 may performadditional or different steps to identify precursor candidates from theloaded process variables, including allowing the user to select specificprocess variables as precursor candidates.

At step 520 of FIG. 5A, the parametric analyzer 500 then builds amultiple-input single-output (MISO) dynamic parametric model between theprecursor variable candidates and the event-relevant KPIs for performingquantitative analysis of the precursor variable candidates. The MISOmodel allows for a more granular investigation of the impact of theprecursor variable candidates on the undesirable plant event. Theprecursor variable candidates may be used as model input and theevent-relevant KPIs may be used as model output for the MISO model. Inthe embodiment of FIG. 5A, the parametric analyzer 500 first builds aMISO model with subspace identification. Subspace identification is analgorithm to identify dynamic process models from input and outputmeasurements and the algorithm used in this invention is performed asthe following two sub-steps (by the parametric analyzer 500 as part ofstep 520). First, the parametric analyzer 500 defines (assumes) a causalrelationship between the precursor variable candidates and the targetKPI as a linear multi-input single-output (MISO) dynamic state spacemodel as shown in Equations (13) and (14) as follows:X _(n)(t+1)=AX _(n)(t)+Bu(t)  (13)y(t)=CX _(n)(t)+Du(t)  (14)

Next, the parametric analyzer 500 assembles the input and output datainto a special subspace projection format and applies a closed-loopsubspace identification algorithm (see, e.g., Zhao, H., et al.,“Improved Closed Loop Subspace Identification Technology For AdaptiveModeling and APC Sustained Value”, AIChE Spring Meeting, 2012; and Qin,S. Joe, “An overview of subspace identification”, Computers and ChemicalEngineering, vol. 30, pages 1502-1513 (2006), which are bothincorporated herein by reference in their entirety) to solve the problemby Equation (15) as follows:min J({u(t),y(t),X _(n)(t)},θ)=min∥y(t)−ŷ(t)∥₂  (15)subject toX _(n)(t+1)=AX _(n)(t)+Bu(t)y(t)=CX _(n)(t)+Du(t)

Note, the parametric analyzer may analyze the basic x-y linearrelationship between the precursor variable candidates as input and theevent-relevant KPIs as output prior to building the MISO model, forexample, as a simple x-y scatter plot between an input and KPI outputwill be able to show the linearity and deviation. If the parametricanalyzer 500 detects a significant high-nonlinearity between individualprecursor variable candidates based on this analysis, then theparametric analyzer may apply a non-linear transform or piecewise lineartransform to individual precursor variable candidates prior to using thevariables as input to the MISO model, for example, a logarithm transformor piecewise linear transform on the input can correct manynonlinearities.

Next, the parametric analyzer 500 may factor each sub-model of the MISOmodel into accurate dead-times and dynamic linear filters. The model isfactored into accurate dead-times and dynamic filters to describe thecausal relationship between each precursor candidate and event-relevantKPI. At step 530, the parametric analyzer calculates estimates of thedead-times between the precursor candidates and event-relevant KPIs. Atstep 530, the parametric analyzer may calculate the estimates of thedead-times for each input channel of a precursor candidate as input to arespective sub-model of the MISO model. Further, in some embodiments,the parametric analyzer may estimate dead-time for each input channel ofa precursor variable candidate using an optimization search based on thesub-model of the i-th input to the corresponding event-relevant KPI. Forexample, the dead-time search can be resolve in the problem defined inEquations (16) and (17) as follows, where DT_(i) is the ith inputchannel dead-time, y(t) and ŷ(t) is the step response of thesingle-input single output (SISO) sub-model of (17) and an approximationof the response.

$\begin{matrix}{{\min\limits_{{DT}_{i}}{J\left( {\left\{ {{u_{i}(t)},{y(t)}} \right\},\theta} \right)}} = {\min\limits_{{DT}_{i}}{{{y(t)} - {\hat{y}(t)}}}_{2}}} & (16) \\{{{subject}\mspace{14mu}{to}}{{X_{n}\left( {t + 1} \right)} = {{{AX}_{n}(t)} + {{Bu}_{i}\left( {t - {DT}_{i}} \right)}}}{{y(t)} = {{CX}_{n}(t)}}} & (17)\end{matrix}$

At step 540, the parametric analyzer calculates the estimates of optimallinear dynamic filters for each input channel of a precursor variablecandidate as model input to the MISO model. Further, in someembodiments, at step 540, the parametric analyzer further estimates theoptimal linear dynamic filters of each input channel for a precursorvariable candidate using a linear model reduction technique to determinethe optimal low-order model fitting. For example, after dead-time isidentified in Equations (16) and (17), Equation (17) is then furtherreduced into a lower order model, such as first-order or second-orderfilter by using standard model reduction algorithm (Benner, Peter;Fassbender, Heike (2014), “Model Order Reduction: Techniques and Tools”,Encyclopedia of Systems and Control, Springer,doi:10.1007/978-1-4471-5102-9_142-1, ISBN 978-1-4471-5102-9, which isincorporated herein by reference in its entirety). The parametricanalyzer 500 further factors each MISO sub-model into a series ofconnected dead-time units, and in turn a single-input single-output(SISO) state space dynamic sub-model, in which the parametric analyzer500 approximates the model dynamics by a low-order dynamic filterdefined and calculated for each SISO sub-model, the model structure isshown in FIG. 5B.

At step 550 of method 500, the parametric analyzer rebuilds a newparametric linear MISO as a PLS model by applying the estimateddead-times and dynamic linear filters at each individual input channelof each precursor variable candidate. The parametric analyzer may applya non-linear transform or piecewise linear transform to individualprecursor variable candidates prior to using the variables as input tothe rebuilt MISO PLS model. At step 560, the parametric analyzer 500uses the MISO PLS model to perform PLS regression and sensitivitytechniques (see, e.g., Garthwaite, Paul H., “An Interpretation ofPartial Least Squares,” Journal of the American Statistical Associationvol. 89 (425), pages 122-127 (1994), which is incorporated herein byreference in its entirety) for calculating a score for the strength ofcorrelation with the event-relevant KPIs, including a relativesensitivity, contribution, and lead-time for each precursor variablecandidate. Then, the parametric analyzer 500 selects root-causevariables from the precursor candidates with the highest strength ofsensitivity score. The parametric analyzer 500 may select the root-causevariable using fuzzy logic rules which combine the scores for top rankedprecursor variable candidates to determine the most appropriate of thevariables for early risk indication and event prevention actionadvising. At step 570, the parametric analyzer presents (outputs) theselected root-cause variables to process engineers and operators who mayconfirm or reject the variables as the root-cause of the respectiveundesirable plant event based on their process knowledge and experience.

FIG. 5B illustrates a block diagram depicting an example (MISO) model580 used in some embodiments of FIG. 5A (e.g., at step 520 and 550 ofparametric analyzer 500). The MISO model 580 is shown as a summation ofmultiple single-input single-output (SISO) dynamic sub-models, but inother embodiments the MISO model may be organized in various othersub-model configurations. The precursor variable candidates are providedas input 521, 522, 523 to the input channel for each respectivesub-model of the MISO model and the event-relevant KPIs is provided asoutput 577 of the MISO model. The input 521, 522, 523 are provided todead-time units 541, 542, 543 of the respective sub-model forcalculating dead-time estimates for the precursor variable candidatesbased on the corresponding event-relevant KPIs. The dead-time estimatesare then part of the model parameters from the dead-time units 541, 542,543. The input 521, 522, 523 go through the dead-time units 541, 542,543 with corresponding time delays, then input to low-order dynamicfilters 551, 552, 553 to estimate the optimal linear filter for theprecursor variable candidates. The optimal linear filter estimates arethen part of the model parameters from low-order dynamic filters 551,552, 553. The outputs from optimal linear filters are then summed 561with a weighting vector that is a partial-least squares (PLS) modelfitted with the inputs and output KPI data for the precursor variablecandidates, which provides a relative sensitivity and contributionranking score for each precursor variable candidate. Then fuzzy logicrules are applied to combine the scores for top ranked precursorvariable candidates to select root-cause variables. The selected rootcause variables provided to process engineers and operators to confirmor reject the variables as the root-cause of the respective undesirableplant event. In some embodiments, the control system, such as the DCS orother plant or refinery control system, may automatically confirm orreject the variables as the root cause and programs the control systemto take actions (e.g., monitor, alarm, update plant variables, and such)based on the confirmed root cause variables. For example, outputrepresenting implications of the root-cause precursor variables may betransmitted to a plant control system for diagnosing a root-cause of theevent

Using this output information 577 as a signature, plant operations maybe diagnosed and root causes detected. In turn, the process engineersand operators (or plant control systems) may quickly narrow the resultsof the root-cause search of the undesirable plant event, and determinewhether each root-cause variable candidates is the cause of an operationproblem based on their knowledge of the process. Further, the processengineers and operators, or the control system (e.g., DCS), may also usethis information to better understand unexpected events, to be morefocused on particular process variables as precursors, and to take earlyactions if necessary based on KPIs' monitoring and alarming. As aresult, the root-cause-analysis lowers the risks of reoccurrence of theevents, and as such, prevents the undesirable events from futureoperations. In this way, embodiments of the present invention provideprocess engineers and operators (along with programmed plant control andprocessing systems) with a new and highly efficient tool for eventcausality analysis in various applications of process industry,including but not limited to undesirable events root-cause analysis,operational trouble-shooting, process faults detection andidentification, as well as plant risk management.

Example Root Cause Analysis

FIGS. 6A-6I illustrate performing root cause analysis of flooding in aC2 splitter, a distillation column of the ethylene manufacturingprocess, using the methods 200, 400, 500, 580 of FIGS. 2A, 4, and 5A-5B.As shown in FIG. 6A, there are more than forty thousand C2 splitterdistillation columns in the U.S. alone, and a significant number ofthese distillation columns experience flooding. The flooding of adistillation column limits the capacity of the column, and may result inan unwanted plant shutdown. However, performing root-cause-analysis onC2 splitter flooding using the numerous process variable measurementsacross the plant, such as the 2,000 process variable measurementspotentially related to the C2 splitter flooding in FIG. 6A, presents achallenge for process engineers and operators. To address this issue,the present invention determines one or more previously unmeasuredindicators for identifying particular process variables for root causeanalysis of the C2 splitter flooding. In this way, embodiments of thepresent invention provide an analyzer for C2 splitter distillationcolumns and the like.

FIGS. 6B-6D illustrate the generation of continuous KPIs for use as theindicators of C2 splitter flooding for performing root-cause-analysis.FIGS. 6B-6D may generate the continuous KPIs in accordance with examplemethod 200 of FIG. 2A. FIG. 6B illustrates the first step in generatingthe continuous KPIs using a first principles model. The step of FIG. 6Bcollects historical process variable measurements relevant to C2splitter flooding for developing a calibrated process model (i.e., afirst principles model). The first principles model then estimates C2splitter flooding relevant KPI values periodically when the plant is ata steady-state. FIG. 6C illustrates the second step in generating thecontinuous KPIs using an inferential model. The step of FIG. 6C uses rawand derived process variable measurements for building a trainedinferential model. The trained inferential model then estimates C2splitter flooding relevant KPI values continuously between steady-stateregimes. As shown in FIG. 6D, the first principles model and inferentialmodel are used together over a time series as a hybrid model to generatecontinuous KPIs for predicting C2 splitter flooding behavior. Thecalibrated first principles model is used by the hybrid analyzer 260,300 to calculate the KPI values at steady-state over the time period,and the hybrid analyzer 260, 300 uses the inferential model tointerpolate the KPI values between the steady-state calculated values.The hybrid model is adaptive to subsequent steady-state updates andextrapolates until the next steady state KPI values occur. The hybridmodel predicts future C2 splitter behavior by predicting future KPIvalues using the combined first principles model calculated KPI valuesat steady state and the inferential model interpolated KPI values.

FIGS. 6E-6G illustrate the use of the continuously generated KPIsrelevant to C2 splitter flooding to select precursors from the 2,000process variable measurements that may serve as early alerts to C2splitter flooding events. The analysis of the C2 splitter flooding usingthe continuously generated KPIs as shown in FIGS. 6E-6G is performed inaccordance with example method 400 of FIG. 4. As shown in FIG. 6E, asample case study using this analysis reduced the scope of the 2,000process variables potentially relevant to a C2 splitter flooding eventto 20 process variables to be considered as precursor candidates for theevent. Specifically, FIG. 6F illustrates the precursor discoverycomponent that performs a target signal precursor search to reduce thescope of the process variables to 20 precursor candidates. In FIG. 6F,the C2 splitter flooding relevant KPIs are split as a time series bydividing the process variable measurements collected for the KPI over atwo week time period (sampled at 1 minute intervals). The time seriesare then used to perform cross-correlation analysis of the processvariable measurements in accordance with the steps of the example method400 of FIG. 4. The cross-correlation analysis in FIG. 6F identifiesmeasurement time series with negative dead times from a target KPI toindicate the precursor candidates (flood variables) based on calculatedcross-correlation scores for the process variables. Note, as shown,different precursor candidate sets may be discovered for differentoperating regimes.

FIG. 6G illustrates the operating regime discovery process foridentifying precursor sets for different operating regimes. In FIG. 6G,time series sliding windows, along with an unsupervised learningalgorithm, are used to identify clusters of precursor sets for sampleoperating regimes 1, 4, and 6 as shown. Only process variablesdetermined as having high correlation scores for predicting floodvariables in the cross-correlation analysis shown in FIG. 6F are usedidentify the clusters of precursor sets in FIG. 6G. The precursordiscovery processes then further reduce the number of process variablesfrom the precursor sets shown to 20 remaining precursor candidates thathighly correlate to C2 splitter flooding.

FIGS. 6H-6I illustrate processing of the 20 precursor candidatesremaining after the above discovery processes to further reduce theprocess variables to an even smaller set of precursor candidates basedon causality analysis. The processing includes building a parametricmodel that calculates a score for each remaining process variable(identified as a precursor candidate) based on its strength ofcorrelation to the C2 splitter flooding relevant KPIs. As shown in FIG.6H, the strength of correlation is determined based on relativesensitivity (i.e., relevant effect of the measurement on each respectiveKPI), which in the sample field study reduces the number of processvariables to 4 remaining candidates. The strength of correlation isfurther determined based on event lead-time (e.g., response time delaybetween the measurement and the respective KPI response), which reducesthe number of process variables to 1 dominating candidate (FU3006PV—flowrate of the upstream feed to Deethanizer). As shown in FIG. 6H, theresulting model predictions for the C2 splitter flooding relevant KPImatches the KPI data well. FIG. 6I illustrates that from this analysis(of FIGS. 6A-6H) that the FU3006PV upstream flow rate of feed toDeethanizer should be used as the precursor for predicting C2 splitterflooding in the plant.

Digital Processing Environment

FIG. 7 is a simplified block diagram of a computer-based system 720 forperforming root-cause-analysis according to some embodiments of thepresent invention detailed above. The system 720 comprises a bus 725.The bus 725 serves as an interconnector between the various componentsof the system 720. Connected to the bus 725 is an input/output deviceinterface 728 for connecting various input and output devices such as akeyboard, mouse, display, speakers, controller, etc. to the system 720.A central processing unit (CPU) 722 is connected to the bus 725 andprovides for the execution of computer instructions. Memory 727 providesvolatile storage for data used for carrying out computer instructions.Storage 726 provides non-volatile storage for software instructions,such as an operating system (not shown). In particular, memory 727and/or storage 726 are configured with program instructions implementingmethods 200, 400, and 500 for calibrating the first principle model andtraining the inferential model in FIG. 2A, searching for precursorcandidates in FIG. 4, and determining root-cause variables in FIGS.5A-5B. The system 720 also comprises a network interface 721 forconnecting to any variety of networks known in the art, including cloud,wide area networks (WANs) and local area networks (LANs).

Further connected to the bus 725 is a first principles primary analyzermodule 723. The first principles primary analyzer module 723 calculatesKPIs using the first principles model 268, 302 when in steady-state asdetailed above in FIGS. 2B and 3A-3B. The first principles primaryanalyzer module 723 may calibrate the first principles model 268, 302 tocalculate KPIs by any means known in the art. For example, the firstprinciples primary analyzer module 723 may reference training data thatis stored on the storage device 726 or memory 727, such as a historiandatabase, for calibrating the model 268, 302. For further example, thefirst principles primary analyzer module 723 may receive input data fromany point communicatively coupled to the system 720 via the networkinterface 721 and/or input/output device interface 728.

The system 720 further comprises a secondary inferential analyzer module724 that is communicatively/operatively coupled to the first principlesprimary analyzer module 723. The secondary inferential analyzer module724 is configured to generate continuous estimates of KPIs as describedabove in FIGS. 2A-3B. The secondary inferential analyzer module 724 mayestimate KPIs through any means known in the art. For example, thesecondary inferential analyzer module 724 may access historical data,such as in an array, on the storage device 726 or memory 727. Foranother example, the secondary inferential analyzer module 724 mayprocess input data using the CPU 722 via the bus 725. For furtherexample, the target optimizer module 724 may retrieve the input datafrom any point of the plant communicatively coupled to the system 720via the network interface 721 and/or input/output device interface 728.

The system 720 further comprises a search engine 733 and parametricanalyzer 735 as part of a root-cause analysis module 736 that iscommunicatively/operatively coupled to the first principles primaryanalyzer module 723 and secondary inferential analyzer module 724. Thefirst principles primary analyzer module 723 and secondary inferentialanalyzer module 724 from hybrid analyzer 731, such as at 260, 300detailed above in FIGS. 2A-3A, of the root cause analyzer 736. Rootcause analyzer (analyzer module) 736 performs and operates as describedfor computers 101, 102 in FIG. 1. The search engine 733 performs themethod 400 of FIG. 4, but may determine precursor candidates fromprocess variables through any means known in the art. Further, theparametric analyzer 735 filters the precursor candidates to determineroot-cause values as detailed in method 500 of FIG. 5 or through anymeans know in the art. For example, the search engine 733 and parametricanalyzer 735 may access historical data, such as in an array, on thestorage device 726 or memory 727. For another example, the search engine733 and parametric analyzer 735 may process KPI data from theinferential analyzer module 724 using the CPU 722 via the bus 725. Forfurther example, the search engine 733 and parametric analyzer 735 mayretrieve the input data from any point of the plant communicativelycoupled to the system 720 via the network interface 721 and/orinput/output device interface 728.

It should be understood that the example embodiments described hereinmay be implemented in many different way. In some instances, the variousmethods and machines described herein may each be implemented by aphysical, virtual, or hybrid general purpose computer, such as thecomputer system 720. The computer system 720 may be transformed into themachines that execute the methods described herein, for example, byloading software instructions into either memory 727 or non-volatilestorage 726 for execution by the CPU 722. Further, while the principlesprimary analyzer 723, secondary inferential analyzer module 724, searchengine module, and parametric analyzer module are shown as separatemodules, in an example embodiment these modules may be implemented usinga variety of configurations, included implemented together as aroot-cause analyzer module.

The system 720 and its various components may be configured to carry outany embodiments of the present invention described herein. For example,the system 720 may be configured to carry out the methods 200, 400, and500 described hereinabove in relation to FIGS. 2A, 4, and 5A. In anexample embodiment, the first principle primary analyzer module 723,secondary inferential analyzer module 724, search engine 733, andparametric analyzer 735 may be implemented in software that is stored onthe memory 727 and/or storage device 726. In such an example embodiment,the CPU 722 and the memory 727 with computer code instructions stored onthe memory 727 and/or storage device 726 implement a first principleprimary analyzer module that calculates KPI values. Further, thecomponents of the system 720 implement a secondary inferential analyzermodule that is operatively coupled to generate continuous estimates ofKPIs. In addition, the components of the system 720 implement a searchengine that is operatively coupled to search for precursor candidatesfrom the measured process variables based on the KPIs, and implement aparametric analyzer that is operatively coupled to filter the precursorcandidates to determine root-cause variables for analysis.

FIG. 8 illustrates a computer network environment 860 in which anembodiment of the present invention may be implemented. In the computernetwork environment 860, the server 831 is linked through thecommunications network 832 to the clients 833 a-n. The environment 860may be used to allow the clients 833 a-n, alone or in combination withserver 831, to execute any of the methods described hereinabove. Itshould be understood that the example embodiments described above may beimplemented in many different ways. In some instances, the variousmethods and machines described herein may each be implemented by aphysical, virtual, or hybrid general purpose computer, or a computernetwork environment such as the computer environment 860.

Embodiments or aspects thereof may be implemented in the form ofhardware, firmware, or software. If implemented in software, thesoftware may be stored on any non-transient computer readable mediumthat is configured to enable a processor to load the software or subsetsof instructions thereof. The processor then executes the instructionsand is configured to operate or cause an apparatus to operate in amanner as described herein.

Further, firmware, software, routines, or instructions may be describedherein as performing certain actions and/or functions of the dataprocessors. However, it should be appreciated that such descriptionscontained herein are merely for convenience and that such actions infact result from computing devices, processors, controllers, or otherdevices executing the firmware, software, routines, instructions, etc.

It should be understood that the flow diagrams, block diagrams, andnetwork diagrams may include more or fewer elements, be arrangeddifferently, or be represented differently. But it further should beunderstood that certain implementations may dictate the block andnetwork diagrams and the number of block and network diagramsillustrating the execution of the embodiments be implemented in aparticular way.

Accordingly, further embodiments may also be implemented in a variety ofcomputer architectures, physical, virtual, cloud computers, and/or somecombination thereof, and, thus, the data processors described herein areintended for purposes of illustration only and not as a limitation ofthe embodiments.

The teachings of all patents, published applications and referencescited herein are incorporated by reference in their entirety.

EXEMPLARY

The following pseudo code may be used for implementing exampleembodiments of the present invention.

function find_precursors(fld_var_lbl, active_variables, time_series_df,search_setup):    sel_active_variables =cut_low_global_corr(time_series_df, active_variables,   search_setup)   sel_cont_active_variables = cut_discrete_ts(time_series_df,sel_active_variables,   search_setup)    tser =time_series_df[fld_var_lbl].iloc[search_setup.ts_range_min :  search_setup.ts_range_max]   acceptable_ranges = select_ranges(tser,search_setup, search_setup.ts_range_min)    (cum_corr_vec,lag_index_dict) = init_lag_storage(search_setup)    cum_corr_tags =init_3d_df( cum_corr_vec, len(acceptable_ranges), False)   cum_corr_vals = init_3d_df( cum_corr_vec, len(acceptable_ranges))   range_ind = 0    for u in acceptable_ranges:     update_correlated(time_series_df,          sel_cont_active_variables,         cum_corr_tags, cum_corr_vals,          range_ind,         u[1], u[2],          lag_index_dict,          search_setup)          range_ind += 1    precursor_data =select_precursor_tags(time_series_df,              cum_corr_tags,cum_corr_vals,              acceptable_ranges,             lag_index_dict,              search_setup)     return{‘refined_pred_list’: precursor_data[0], ‘pred_dict’: precursor_data[1],  ‘pred_corr’:precursor_data[2]} functioncut_low_global_corr(time_series_df, active_variables, analysis_obj): passed_corr = [ ]  vec_base =time_series_df[active_variables[−1]].values  for u inactive_variables[:−1]:   vec_u = time_series_df[u].values   cr =corrcoef( vec_u, vec_base )[0][1]   ifabs(cr)>analysis_obj.full_ts_corr_cutoff:    passed_corr.append(u) return passed_corr function cut_discrete_ts(time_series_df,active_variables, analysis_obj):    passed = [ ]    for u inactive_variables:     if len( set( time_series_df[u].values ) ) >analysis_obj.min_discr_levels:      passed.append(u)    return passedfunction select_ranges( tser, analysis_obj, start_indx=0 ):    ser_len =len(tser)    abs_cutoff_large_moves = analysis_obj.cutoff_large_moves *timeseries_range(0,   ser_len, tser)    return list_timeseries_ranges(         abs_cutoff_large_moves,          range(0, ser_len,analysis_obj.chunk_length),          tser,          start_indx )function list_timeseries_ranges( cutoff, lst, ser, start_indx=0 ):    v= [ ]    for i in range(1,len(lst)):     range_i =timeseries_range(lst[i−1], lst[i], ser)     if range_i>cutoff:     v.append( [i, lst[i−1]+start_indx, lst[i]+start_indx, range_i] )   return v function init_lag_storage( analysis_obj ):    cum_corr_vec =[ ]    lag_index_dict = { }    ii = 0    for u inrange(analysis_obj.min_lag, analysis_obj.max_lag,analysis_obj.lag_interval):     if u!=0:      cum_corr_vec.append(analysis_obj.select_cnt )      lag_index_dict[ii] = u      ii += 1   return (cum_corr_vec, lag_index_dict) functioninit_3d_df(rowset_lengths, column_count, numeric=True):    first_idx = []    second_idx = [ ]    rr_i = 0    for rr in rowset_lengths:    first_idx = first_idx + [rr_i for u in range(rr)]     second_idx =second_idx + range(rr)     rr_i += 1    multi_idx = [first_idx,second_idx]    column_idx = range(column_count)    if numeric:    return DataFrame( \      zeros(( len(first_idx),column_count)), \     index=MultiIndex.from_tuples(zip(*multi_idx)), \     columns=column_idx)    else:     vec_of_vec=[ ]     for j inrange(len(first_idx)):      vec_of_vec.append( [“ for u inrange(column_count)] )     return DataFrame( \         vec_of_vec, \        index=MultiIndex.from_tuples(zip(*multi_idx)), \        columns=column_idx) function update_correlated(time_series_df,active_variables, cum_corr_tags, cum_corr_vals, range_ind, ind_0, ind_1,lag_index_dict, analysis_obj):    for u in lag_index_dict.items( ):    lag_ind = u[0]     k = u[1]     corr_summary =get_highest_corr(time_series_df, active_variables,  analysis_obj.select_cnt, k, ind_0, ind_1)     tags_k=[ ]     vals_k=[]     for u in corr_summary:      tags_k.append( u[0] )     vals_k.append( u[1] )     cum_corr_tags[range_ind][lag_ind] =tags_k     cum_corr_vals[range_ind][lag_ind] = vals_k functionget_highest_corr(time_series_df, active_variables, n, shift_k,hist_start, hist_end):  high_corr = [ ]  label_list =time_series_df.columns.values  vec_base =time_series_df[label_list[−1]].values[hist_start:hist_end]  for u inactive_variables[:−1]:   vec_u =time_series_df[u].values[hist_start:hist_end]   cr = corrcoef(vec_u[:(−shift_k)] , vec_base[shift_k:])[0][1]   high_corr.append([u,cr])  high_corr.sort(key=lambda x: abs(x[1]), reverse=True)  returnhigh_corr[:n] function select_precursor_tags(time_series_df,cum_corr_tags, cum_corr_vals, acceptable_ranges, lag_index_dict,analysis_obj,        lag_indices_post=None, lag_indices_pre=None,lst_ranges=None, cutoff_signal=None, post_corr_tol=None ):  iflst_ranges==None:   lst_ranges = range(0, len(acceptable_ranges))  iflag_indices_pre==None:   lag_indices_pre = [ ]   for u inlag_index_dict.items( ):    if u[1]>0:     lag_indices_pre.append(u[0]) if lag_indices_post==None:   lag_indices_post = [ ]   for u inlag_index_dict.items( ):    if (u[1]<0) and(abs(u[1])>analysis_obj.cross_corr_post_grace):    lag_indices_post.append(u[0])  sorted_pair_post =sort_cumul_corr(cum_corr_tags, cum_corr_vals, lag_indices_post,lst_ranges, abs, analysis_obj)  post_dict = { }  for u insorted_pair_post:   post_dict[u[0]] = u[1]  sorted_pairs_pred =sort_cumul_corr(cum_corr_tags, cum_corr_vals, lag_indices_pre,lst_ranges, analysis_obj.pred_transform, analysis_obj)  predict_dict = {}  for u in sorted_pairs_pred:   predict_dict[u[0]] = u[1]  ifcutoff_signal==None:   if len(sorted_pair_post) == 0:    cutoff_signal =0.00001   else:    cutoff_signal = 0.1 * abs( sorted_pair_post[0][1] ) if post_corr_tol==None:   post_corr_tol = 0.2 * cutoff_signal pred_dict = { }  pred_list = [ ]  for pred in sorted_pairs_pred:  pred_tag = pred[0]   pred_val = pred[1]   post_val = safe_dict_value(post_dict, pred_tag )   if analysis_obj.pre_to_post_cond != ‘relative’:   if (abs(pred_val)> cutoff_signal) and (abs(post_val)<post_corr_tol):    pred_list.append(pred_tag)     pred_dict[pred_tag] = pred_val  else:    if abs(post_val)<1.e−14:     pred_list.append(pred_tag)    pred_dict[pred_tag] = pred_val    else:     ifabs(pred_val/post_val) >= (1.0/analysis_obj.pre_to_post_tol):     pred_list.append(pred_tag)       pred_dict[pred_tag] = pred_val   df_corr = time_series_df[pred_list].corr( )    refined_pred_list = []    ii = 0    for tag_name in pred_list:   seq = df_corr[tag_name]   if(ii==0) or (max(abs( seq[:ii] )) < analysis_obj.precursor_max_corr):   refined_pred_list.append(tag_name)   ii +=1  return(refined_pred_list, pred_dict, df_corr) functionsort_cumul_corr(cum_corr_tags, cum_corr_vals, lag_indices, lst_ranges,transform, analysis_obj):  lag_corr = { }  for i_range in lst_ranges:  for lag_index in lag_indices:    for j inrange(analysis_obj.select_cnt):     tag_name =cum_corr_tags[i_range][lag_index][j]     tag_corr =cum_corr_vals[i_range][lag_index][j]     if not np.isnan(tag_corr):     if tag_name in lag_corr.keys( ):       lag_corr[ tag_name ] +=transform(tag_corr)      else:       lag_corr[ tag_name ] =transform(tag_corr)   i_range += 1  uvec = [u for u in lag_corr.items()]  uvec.sort(key=lambda x: abs(x[1]), reverse=True)   return uvec

PLS algorithm function PLS1(X, y, l)  2 X⁽⁰⁾ ← X  3 w⁽⁰⁾ ←X^(T)y/||X^(T)y||, an initial estimate of w.  4 t⁽⁰⁾ ← Xw⁽⁰⁾  5 for k =0 to l  6  t_(k) ← t^((k)) ^(T) t^((k)) (note this is a scalar) 7  t^((k)) ← t^((k))/t_(k)  8  p^((k)) ← X^((k)) ^(T) t^((k))  9  q_(k)← y^(T)t^((k)) (note this is a scalar) 10  if q_(k) = 0 11   l ← k,break the for loop 12  if k < l 13   X^((k+1)) ← X^((k)) −t_(k)t^((k))p^((k)) ^(T) 14   w^((k+1)) ← X^((k+1)) ^(T) y 15  t^((k+1)) ← X^((k+1))w^((k+1)) 16 end for 17 define W to be the matrixwith columns w⁽⁰⁾, w⁽¹⁾, ..., w^((l−1)).  Do the same to form the Pmatrix and q vector. 18 B ← W(P^(T)W)⁻¹ q 19 B₀ ← q₀ − P⁽⁰⁾ ^(T) B 20return B, B₀

While this invention has been particularly shown and described withreferences to example embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

What is claimed is:
 1. A computer-implement method for predicting key performance indicators (KPIs) of an industrial process, the method comprising: building a hybrid model that models a subject industrial process, the hybrid model comprising a first-principles model as a primary model and an inferential model as a secondary model; calibrating the first principles model of the built hybrid model in an offline configuration, including: selecting at least one measurable variable from process variables for the subject industrial process, loading process data for the selected at least one measurable variable, and training the first principles model using the loaded process data, said training enables the first principles model to calculate KPI values when the subject industrial process reaches a steady-state period; calibrating the inferential model of the built hybrid model in an offline configuration, including: selecting a model structure to define the input-output data relationship of the inferential model, and based on the selected model structure, training the inferential model using: (i) the loaded process data as input, and (ii) steady-state KPI values calculated by the calibrated first principles model as output, said training enables the inferential model to continuously estimate KPI values when the subject industrial process reaches a non-steady-state period; and deploying the hybrid model online, the deployed hybrid model continuously predicting KPI values during steady-state and non-steady-state periods of the subject industrial process; wherein calibrating the first principles model further includes at least one of: tuning model parameters of the first principles model to at least satisfy a hydraulic mass balance and a heat energy balance at a steady-state operating point; and tuning model parameters of the first principles model in a manner that minimizes model prediction errors in calculating KPI values, wherein the model parameters are tuned based on one or more constraints of the subject industrial process.
 2. The method of claim 1, wherein the loaded process data comprises historical operating measurements collected for the selected at least one measurable variable, the historical operating measurements being retrieved from a historian database.
 3. The method of claim 1, wherein an auto-data-slicing technique is applied to the loaded process data, the auto-data-slicing technique identifying and removing bad quality data from the loaded process data prior to use in calibrating the first principles model and the inferential model.
 4. The method of claim 1, further comprising: dynamically adjusting prediction bias in the inferential model of the deployed hybrid model, the dynamic adjusting being performed using KPI values calculated online during the steady-state periods.
 5. The method of claim 1, further comprising: detecting an operational change in the subject industrial process; and in response to the detection, automatically re-calibrating the first principles model and the inferential model, wherein the re-calibrating maintains online performance of the hybrid model in predicting KPI values.
 6. The method of claim 1, wherein the inferential model is one of a partial least squares (PLS) model, piecewise linear transformed nonlinear model, monotonic neural network (NN) model, or bounded derivative network (BDN) model.
 7. The method of claim 1, wherein training the inferential model further comprises: creating a dataset including: (a) the loaded process data, and (b) the steady-state KPI values calculated by the calibrated first principles model; dividing the dataset into a training set and a validation set; fitting the inferential model with the training set; and using the validation set to minimize prediction error in the fitted inferential model.
 8. The method of claim 1, further comprising: applying the continuously predicted KPI values to predict behavior of the subject industrial process.
 9. A computer system for predicting key performance indicators (KPIs) of an industrial process, the system comprising: at least one processor coupled to computer memory, the at least one processor configured to: build a hybrid model that models a subject industrial process, the hybrid model comprising a first-principles model as a primary model and an inferential model as a secondary model; calibrate the first principles model of the built hybrid model in an offline configuration, including: selecting at least one measurable variable from process variables for the subject industrial process, loading process data for the selected at least one measurable variable, and training the first principles model using the loaded process data, said training enables the first principles model to calculate KPI values when the subject industrial process reaches a steady-state period; calibrate the inferential model of the built hybrid model in an offline configuration, including: selecting a model structure to define the input-output data relationship of the inferential model, and based on the selected model structure, training the inferential model using: (i) the loaded process data as input, and (ii) steady-state KPI values generated by the calibrated first principles model as output, said training enables the inferential model to continuously estimate KPI values when the subject industrial process reaches a non-steady-state period; and deploy the hybrid model online, the deployed hybrid model continuously predicting KPI values during steady-state and non-steady-state periods of the subject industrial process; wherein the at least one processor is further configured to calibrate the first principles model by at least one of: tuning model parameters of the first principles model to at least satisfy a hydraulic mass balance and a heat energy balance at a steady-state operating point; and tuning model parameters of the first principles model in a manner that minimizes model prediction errors in calculating KPI values, wherein the model parameters are tuned based on one or more constraints of the subject industrial process.
 10. The system of claim 9, wherein the loaded process data comprises historical operating measurements collected for the selected at least one measurable variable, the historical operating measurements being retrieved from a historian database.
 11. The system of claim 9, wherein the at least one processor is further configured to: apply an auto-data-slicing technique to the loaded process data, the auto-data-slicing technique identifying and removing bad quality data from the loaded process data prior to use in calibrating the first principles model and the inferential model.
 12. The system of claim 9, wherein the at least one processor is further configured to: dynamically adjust prediction bias in the inferential model of the deployed hybrid model, the at least one processor dynamic adjusts the prediction bias using KPI values calculated online during the steady-state periods.
 13. The system of claim 9, wherein the at least one processor is further configured to: detect an operational change in the subject industrial process; and in response to the detection, automatically re-calibrate the first principles model and the inferential model, wherein the re-calibrating maintains online performance of the hybrid model in predicting KPI values.
 14. The system of claim 9, wherein the inferential model is one of a partial least squares (PLS) model, piecewise linear transformed nonlinear model, monotonic neural network (NN) model, or bounded derivative network (BDN) model.
 15. The system of claim 9, wherein the at least one processor is further configured to train the inferential model by: creating a dataset including: (a) the loaded process data, and (b) the steady-state KPI values calculated by the calibrated first principles model; dividing the dataset into a training set and a validation set; fitting the inferential model with the training set; and using the validation set to minimize prediction error in the fitted inferential model.
 16. The system of claim 9, wherein the at least one processor is further configured to: apply the continuously predicted KPI values to predict behavior of the subject industrial process.
 17. A computer program product comprising: a non-transitory computer-readable storage medium having code instructions stored thereon, the storage medium operatively coupled to a processor, such that, when executed by the processor to predict key performance indicators (KPIs) of an industrial process, the computer code instructions cause the processor to: build a hybrid model that models a subject industrial process, the hybrid model comprising a first-principles model as a primary model and an inferential model as a secondary model; calibrate the first principles model of the built hybrid model in an offline configuration, including: selecting at least one measurable variable from process variables for the subject industrial process, loading process data for the selected at least one measurable variable, and training the first principles model using the loaded process data, said training enables the first principles model to calculate KPI values when the subject industrial process reaches a steady-state period; calibrate the inferential model of the built hybrid model in an offline configuration, including: selecting a model structure to define the input-output data relationship of the inferential model, and based on the selected model structure, training the inferential model using: (i) the loaded process data as input, and (ii) steady-state KPI values generated by the calibrated first principles model as output, said training enables the inferential model to continuously estimate KPI values when the subject industrial process reaches a non-steady-state period; and deploy the hybrid model online, the deployed hybrid model continuously predicting KPI values during steady-state and non-steady-state periods of the subject industrial process; wherein the computer code instructions further include code instructions that cause the processor to calibrate the first principles model by at least one of: tuning model parameters of the first principles model to at least satisfy a hydraulic mass balance and a heat energy balance at a steady-state operating point; and tuning model parameters of the first principles model in a manner that minimizes model prediction errors in calculating KPI values, wherein the model parameters are tuned based on one or more constraints of the subject industrial process. 